wehub-resource-sync
e4dcfc49aa
Tests / Import Check (Python 3.13) (push) Has been cancelled
Tests / Import Check (Python 3.14) (push) Has been cancelled
Tests / Python Tests (Python 3.11) (push) Has been cancelled
Tests / Python Tests (Python 3.12) (push) Has been cancelled
Tests / Python Tests (Python 3.14) (push) Has been cancelled
Tests / Test Summary (push) Has been cancelled
Tests / Lint and Format (push) Has been cancelled
Tests / Web Node Tests (push) Has been cancelled
Tests / Import Check (Python 3.11) (push) Has been cancelled
Tests / Import Check (Python 3.12) (push) Has been cancelled
Tests / Python Tests (Python 3.13) (push) Has been cancelled
95 lines
3.6 KiB
HTML
95 lines
3.6 KiB
HTML
<!DOCTYPE html>
|
|
<html lang="zh-CN">
|
|
<head>
|
|
<meta charset="UTF-8">
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
|
<title>深度学习基础 - 测试文档</title>
|
|
</head>
|
|
<body>
|
|
<header>
|
|
<h1>深度学习基础教程</h1>
|
|
<p>本文档用于RAG管道测试</p>
|
|
</header>
|
|
|
|
<main>
|
|
<article>
|
|
<h2>什么是深度学习？</h2>
|
|
<p>深度学习（Deep Learning）是机器学习的一个分支，它使用多层神经网络来学习数据的层次化表示。深度学习在图像识别、自然语言处理和语音识别等领域取得了突破性进展。</p>
|
|
|
|
<h3>神经网络的基本组成</h3>
|
|
<ul>
|
|
<li><strong>输入层</strong>：接收原始数据</li>
|
|
<li><strong>隐藏层</strong>：进行特征提取和转换</li>
|
|
<li><strong>输出层</strong>：产生最终预测结果</li>
|
|
</ul>
|
|
|
|
<h3>常见的深度学习模型</h3>
|
|
<table border="1">
|
|
<thead>
|
|
<tr>
|
|
<th>模型类型</th>
|
|
<th>主要应用</th>
|
|
<th>特点</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>卷积神经网络 (CNN)</td>
|
|
<td>图像分类、目标检测</td>
|
|
<td>局部感受野、参数共享</td>
|
|
</tr>
|
|
<tr>
|
|
<td>循环神经网络 (RNN)</td>
|
|
<td>序列建模、时间序列预测</td>
|
|
<td>处理变长序列、记忆功能</td>
|
|
</tr>
|
|
<tr>
|
|
<td>Transformer</td>
|
|
<td>自然语言处理、大语言模型</td>
|
|
<td>自注意力机制、并行计算</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
|
|
<h2>深度学习的训练过程</h2>
|
|
<ol>
|
|
<li>数据预处理：清洗、归一化、数据增强</li>
|
|
<li>前向传播：计算预测值</li>
|
|
<li>损失计算：比较预测值与真实值</li>
|
|
<li>反向传播：计算梯度</li>
|
|
<li>参数更新：使用优化器更新权重</li>
|
|
</ol>
|
|
|
|
<h3>常用激活函数</h3>
|
|
<p>激活函数为神经网络引入非线性，常用的激活函数包括：</p>
|
|
<ul>
|
|
<li><code>ReLU</code>: f(x) = max(0, x)</li>
|
|
<li><code>Sigmoid</code>: f(x) = 1 / (1 + e^(-x))</li>
|
|
<li><code>Tanh</code>: f(x) = (e^x - e^(-x)) / (e^x + e^(-x))</li>
|
|
<li><code>Softmax</code>: 用于多分类问题的输出层</li>
|
|
</ul>
|
|
|
|
<blockquote>
|
|
<p>"深度学习的成功在于其能够自动学习特征表示，而无需人工设计特征。"</p>
|
|
<footer>—— Yann LeCun, Geoffrey Hinton, Yoshua Bengio</footer>
|
|
</blockquote>
|
|
</article>
|
|
|
|
<aside>
|
|
<h3>相关资源</h3>
|
|
<nav>
|
|
<ul>
|
|
<li><a href="https://pytorch.org">PyTorch 官方文档</a></li>
|
|
<li><a href="https://tensorflow.org">TensorFlow 官方文档</a></li>
|
|
<li><a href="https://keras.io">Keras 教程</a></li>
|
|
</ul>
|
|
</nav>
|
|
</aside>
|
|
</main>
|
|
|
|
<footer>
|
|
<p>© 2024 DeepTutor 测试文件 | 仅用于单元测试</p>
|
|
</footer>
|
|
</body>
|
|
</html>
|